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Influence of tensile properties on hole expansion ratio investigated using a generative adversarial imputation network with explainable artificial intelligence

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Abstract

Hole expansion ratio is widely used to estimate the stretch flangeability of sheet metals which is a critical property of formability and to evaluate the efficiency of a forming process. Although many experiments were conducted in the past to identify the key tensile properties affecting hole expansion ratio, their results failed due to the data scarcity. This work demonstrates a machine learning framework coupled with imputation methods to augment both the quantity and quality of collected experimental data. Especially, a generative adversarial imputation network (GAIN) is used to impute the missing tensile properties in the collected experimental data. With the imputed data, the hole expansion ratio is predicted through an extra tree regressor. In terms of the imputation performance, GAIN resulted in the lowest root mean square error of 0.09146 when 50 known tensile properties are randomly removed and imputed with GAIN. In terms of the hole expansion ratio prediction performance, the extra tree regressor showed the lowest root mean square error of 0.124 compared to other machine learning models. Finally, the influence of each tensile property on the hole expansion ratio is analyzed using Shapley additive explanations, an explainable artificial intelligence technique. In this study, the influences of various tensile properties on hole expansion ratio were quantitatively determined for the first time via machine learning and this analysis will accelerate the exploration of sheet metals with high formability performances.

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Data availability

The datasets generated during the current study are available from the corresponding author on request.

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Acknowledgements

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) [grant number NRF-2021R1A2C3006662] and also this work was supported by POSCO (2022Y006). Yeon Taek Choi was supported by the Basic Science Research Program “Fostering the Next Generation of Researcher” through the NRF funded by the Ministry of Education [grant number 2022R1A6A3A13073824]

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Authors and Affiliations

  1. Department of Materials Science and Engineering, Pohang University of Science and Technology (POSTECH), Pohang, 37673, Republic of Korea

    Jeong Ah Lee, Yeon Taek Choi & Hyoung Seop Kim

  2. Department of Mechanical Engineering, Pohang University of Science and Technology (POSTECH), Pohang, 37673, Republic of Korea

    Jaejung Park & Seungchul Lee

  3. Graduate Institute of Ferrous Technology, Pohang University of Science and Technology (POSTECH), Pohang, 37673, Republic of Korea

    Rae Eon Kim & Hyoung Seop Kim

  4. Department of Materials AI & Big-Data, Korea Institute of Materials Science (KIMS), Changwon-Si, Republic of Korea

    Jaimyun Jung

  5. Institute for Convergence Research and Education in Advanced Technology, Yonsei University, Seoul, 03722, Republic of Korea

    Seungchul Lee & Hyoung Seop Kim

  6. Graduate School of Artificial Intelligence, Pohang University of Science and Technology (POSTECH), Pohang, 37673, Republic of Korea

    Seungchul Lee

  7. Technical Research Lab, POSCO, Incheon, 21985, Republic of Korea

    Min Hong Seo

  8. Advanced Institute for Materials Research (WPI-AIMR), Tohoku University, Sendai, 980-8577, Japan

    Hyoung Seop Kim

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Contributions

JAL contributed to methodology, visualization, and writing—original draft. JP contributed to software, visualization, writing—review ; editing. YTC contributed to investigation. REK contributed to investigation. JJ contributed to writing—review ; editing. SL contributed to writing—review ; editing. HSK contributed to supervision, funding acquisition, writing—review ; editing.

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Correspondence to Jaimyun Jung, Seungchul Lee or Hyoung Seop Kim.

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Lee, J.A., Park, J., Choi, Y.T. et al. Influence of tensile properties on hole expansion ratio investigated using a generative adversarial imputation network with explainable artificial intelligence. J Mater Sci 58, 4780–4794 (2023). https://doi.org/10.1007/s10853-023-08315-8

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